Systems and methods for inline fluid characterization

ABSTRACT

A system performs a method for characterizing passage of a patient fluid through a conduit. The method includes quantifying flow of fluidic content through a conduit, where the fluidic content includes a patient fluid, estimating a concentration of a fluid component of the patient fluid in the fluidic content, and characterizing passage of the patient fluid loss through the conduit based on the quantified flow and the concentration of the fluid component. At least one of the quantified flow or the concentration of the fluid component is based on sensor data from a sensor arrangement coupled to the conduit. Other apparatus and methods are also described.

RELATED APPLICATION

This application claims the priority benefit of U.S. Provisional PatentApplication No. 62/737,730, filed Sep. 27, 2018 and titled “SYSTEMS ANDMETHODS FOR IN-LINE FLUID CHARACTERIZATION,” which is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the technicalfield of special-purpose machines that facilitate characterization offluid passage, which may include estimation of blood loss or gain,including software-configured computerized variants of suchspecial-purpose machines and improvements to such variants, and to thetechnologies by which such special-purpose machines become improved.

BACKGROUND

Inaccurate estimation of fluid passage (e.g., fluids lost, fluidsprocessed, or fluids gained) for a patient, such as during a surgicalprocedure, may put the patient's health at risk on unnecessarily consumemedical resources. For example, where the fluid is blood, overestimationof patient blood loss results in the unnecessary consumption oftransfusion-grade blood, and may lead to downsides, such as unnecessaryclinical risk to the patient and shortages of transfusion-grade bloodthat may be needed for other patients. As another example,underestimation of patient blood loss may lead to delayed resuscitationand transfusion, increased risk of infections, tissue death, or evenpatient death, such as in the event of hemorrhage. Similar effects mayrespectively result from underestimation and overestimation of bloodgain (e.g., from a transfusion). Underestimation or overestimation ofblood processed (e.g., through a dialysis machine) can unnecessarilyprolong such processing or reduce the benefits from such processing.

Furthermore, inaccurate estimation of fluid passage (e.g., fluids lost,fluids processed, or fluids gained) may be a significant contributor tohigh operating costs and high surgical costs for hospitals, clinics, andother medical facilities. In particular, unnecessary blood transfusions,resultant from overestimation of patient blood loss, lead to higheroperating costs for medical institutions. Additionally, delayed bloodtransfusions, resultant from underestimation of patient blood loss, havebeen associated with billions of dollars in avoidable patient infectionsand re-hospitalizations annually. Thus, it may be desirable to have moreaccurate systems and methods for estimating or otherwise characterizingpassage of a patient fluid.

BRIEF DESCRIPTION OF THE DRAWINGS

Some example embodiments are illustrated by way of example and notlimitation in the figures of the accompanying drawings.

FIG. 1 is a schematic diagram illustrating a system for inline fluidcharacterization, according to some example embodiments.

FIG. 2A is a perspective view of a housing for a sensor arrangement,according to some example embodiments.

FIGS. 2B and 2C are cross-sectional views of the housing depicted inFIG. 2A, according to some example embodiments.

FIG. 3A is a schematic diagram illustrating a conduit segment insert,according to some example embodiments.

FIG. 3B is a schematic diagram illustrating a sensor arrangement coupledto the conduit segment insert depicted in FIG. 3A, according to someexample embodiments.

FIG. 4A is a schematic diagram illustrating a pinching mechanism for aconduit, according to some example embodiments.

FIG. 4B is a schematic diagram illustrating another pinching mechanismfor a conduit, according to some example embodiments.

FIG. 5 is schematic diagram illustrating a branched conduit withalternating pinching mechanisms, according to some example embodiments.

FIG. 6A is a schematic diagram illustrating an ultrasound flow sensor,according to some example embodiments.

FIG. 6B is a schematic diagram illustrating another ultrasound flowsensor, according to some example embodiments.

FIG. 7A is schematic diagram illustrating an optical flow sensor,according to some example embodiments.

FIG. 7B is a set of graphs illustrating signal waveforms for detectorsin the optical flow sensor arrangement depicted in FIG. 7A, according tosome example embodiments.

FIG. 8A is a schematic diagram illustrating an optical flow sensorarrangement, according to some example embodiments.

FIG. 8B is a cross-sectional view of the optical flow sensor arrangementdepicted in FIG. 8A, according to some example embodiments.

FIG. 8C is a graph illustrating signal intensity for detectors in theoptical flow sensor arrangement depicted in FIG. 8B, according to someexample embodiments.

FIG. 9 is a cross-sectional view of another optical flow sensorarrangement, according to some example embodiments.

FIG. 10 is a schematic diagram illustrating a thermal mass flow sensor,according to some example embodiments.

FIG. 11 is a schematic diagram illustrating a multispectral imagingarrangement, according to some example embodiments.

FIG. 12 is a flowchart illustrating operation of a system in performinga method of characterizing fluidic contents flowing through a conduit,according to some example embodiments.

FIG. 13 is a diagram illustrating motion tracking of fluidic content,according to some example embodiments.

FIG. 14 is flowchart illustrating logical operations in characterizingfluidic content flowing through a conduit, according to some exampleembodiments.

FIG. 15 is a diagram illustrating optical mass estimation for fluidiccontent, according to some example embodiments.

FIG. 16 is a diagram illustrating estimation of fluid componentconcentration based on particle scattering, according to some exampleembodiments.

FIG. 17 is a graph illustrating a mathematical merge of results fromvarious types of sensors, according to some example embodiments.

FIG. 18 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods (e.g., procedures or algorithms) facilitatecharacterizing passage of a fluid that is flowing within a conduit(e.g., fluids from a patient undergoing a medical procedure, whichfluids may include blood as a fluid component), and example systems(e.g., special-purpose machines configured by special-purpose software)are configured to facilitate characterizing a fluid flowing within aconduit. Examples merely typify possible variations. Unless explicitlystated otherwise, structures (e.g., structural components, such asmodules) are optional and may be combined or subdivided, and operations(e.g., in a procedure, algorithm, or other function) may vary insequence or be combined or subdivided. In the following description, forpurposes of explanation, numerous specific details are set forth toprovide a thorough understanding of various example embodiments. It willbe evident to one skilled in the art, however, that the present subjectmatter may be practiced without these specific details.

Generally, an example method for characterizing passage of a patientfluid includes: accessing sensor data from a sensor arrangement coupledto a conduit through which fluidic content is flowing, where the fluidiccontent includes a patient fluid, quantifying flow of the fluidiccontent through the conduit, estimating a concentration of a fluidcomponent of the patient fluid in the fluidic content, andcharacterizing passage of the patient fluid through the conduit based onthe quantified flow and the concentration of the fluid component. Forexample, in some variations, the characterizing of the passage of thepatient fluid includes quantifying the volume of the patient fluidflowing through the conduit. The flow rate, the concentration of thefluid component, or both, may be determined (e.g., estimated) based onthe sensor data (e.g., sampling data) from the sensor arrangementcoupled to the conduit. In some variations, the patient fluid to becharacterized is blood, and the fluid component whose concentration isestimated is hemoglobin. In some variations, the method includesreducing the flow of the fluidic content through the conduit whilequantifying the flow of the fluidic content, while estimating aconcentration of a fluid component of the patient fluid, or both.

There are various suitable ways to quantify flow of fluidic contentthrough a conduit. In some variations, quantifying the flow of fluidiccontent includes estimating a flow rate of the fluidic content. Forexample, estimating the flow rate may include using an ultrasoundDoppler flow meter to emit ultrasonic waves into the conduit and analyzea frequency shift of ultrasonic waves reflected from the fluidiccontent. As another example, estimating the flow rate may include usinga time-of-flight ultrasound flow meter to emits ultrasonic waves intothe conduit and analyze the flight time of ultrasonic waves transmittedthrough the fluidic content. In another example, estimating the flowrate includes comparing a first optical signal and a second opticalsignal, where the first optical signal corresponds to light detected ata first location along the conduit and the second optical signalcorresponds to light detected at a second location along the conduit.Comparison of the first and second optical signals may be used toestimate the flow rate of the fluidic content in the conduit. In somevariations, quantifying flow of fluidic content includes estimating athermal mass flow of the fluidic content. For example, estimating athermal mass flow of the fluidic content may include introducing a knownamount of heat into a flow of fluidic content and measuring theassociated temperature change (e.g., maintaining a probe at a constanttemperature within the fluidic content and measuring the energy consumedin doing so). As another example, estimating a thermal mass flow of thefluidic content may include introducing a known amount of heat into theflow of fluidic content and measuring the change in temperature of thefluidic content at some point downstream.

Furthermore, there are various suitable ways to estimate theconcentration of a fluid component in the fluidic content. For example,estimating the concentration of a fluid component may include analyzinga multispectral image of the conduit. As another example, estimating theconcentration of a fluid component may include applying a machinelearning algorithm to a color image of the conduit. Furthermore, in somevariations, the method may include performing a spectroscopy analysis todetermine composition of the fluidic content.

Generally, an example system for characterizing passage of patient fluidthrough a conduit includes a conduit configured to convey fluidiccontent that includes a patient fluid, a sensor arrangement couplable tothe conduit, where the sensor arrangement includes at least one sensorconfigured to generate sensor data based on the fluidic content, and oneor more processors configured to perform operations that includeaccessing the sensor data, quantifying flow of the fluidic contentthrough the conduit, estimating a concentration of a fluid component ofthe patient fluid in the fluidic content, and characterizing passage ofthe patient fluid through the conduit based on the quantified flow andthe concentration of the fluid component.

The sensor arrangement may include any suitable form factor configuredfor coupling to the conduit. For example, in some variations, the sensorarrangement may include a housing configured to cover at least a portionof the conduit, and may be configured to clamp onto or slide over theconduit. For example, the housing can include jaws configured to clamponto the conduit. As another example, the system may further include aconduit insert that is couplable inline with the conduit.

The sensor arrangement may include any combination of one or moresuitable sensors for quantifying a flow, determining a concentration ofa fluid component, or both. For example, the sensor arrangement mayinclude an ultrasound flow rate sensor, a thermal mass flow sensor, orany suitable combination thereof. In some variations, the sensorarrangement includes at least one optical sensor configured to detectlight transmitted through the fluidic content. For example, the sensorarrangement may include a plurality of optical sensors arranged at aplurality of axial locations along the conduit, at a plurality ofcircumferential locations around the conduit, or any suitablecombination thereof. In some variations, the sensor arrangement includesone or more optical sensors, thermal sensors, ultrasound sensors, or anysuitable combination thereof. Furthermore, such sensors may include oneor more optical sensor arrays configured to perform multispectral orspectroscopic imaging, color imaging, or both, to facilitate estimatinga concentration of a fluid component.

In some example embodiments, a system includes:

a conduit configured to convey fluidic content that includes a patientfluid;a sensor arrangement coupled to the conduit and including at least onesensor configured to generate sensor data based on the fluidic content;andone or more processors configured to perform operations comprising:accessing the sensor data from the sensor arrangement coupled to theconduit through which the fluidic content is flowing, the fluidiccontent including the patient fluid;quantifying flow of the fluidic content flowing through the conduit;estimating a concentration of a fluid component of the patient fluid inthe fluidic content flowing through the conduit; andcharacterizing passage of the patient fluid through the conduit based onthe quantified flow of the fluidic content and on the estimatedconcentration of the fluid component in the fluidic content, at leastone of the quantifying of the flow or the estimating of theconcentration being based on the sensor data from the sensor arrangementcoupled to the conduit.

In certain example embodiments, a method includes:

accessing sensor data from a sensor arrangement coupled to a conduitthrough which fluidic content is flowing, the fluidic content includinga patient fluid;quantifying flow of the fluidic content flowing through the conduit;estimating a concentration of a fluid component of the patient fluid inthe fluidic content flowing through the conduit; andby one or more processors, characterizing passage of the patient fluidthrough the conduit based on the quantified flow of the fluidic contentand on the estimated concentration of the fluid component in the fluidiccontent, at least one of the quantifying of the flow or the estimatingof the concentration being based on the sensor data from the sensorarrangement coupled to the conduit.

In various example embodiments, a machine-readable medium includesinstructions that, when executed by one or more processors of a machine,cause the machine to perform operations including:

accessing sensor data from a sensor arrangement coupled to a conduitthrough which fluidic content is flowing, the fluidic content includinga patient fluid;quantifying flow of the fluidic content flowing through the conduit;estimating a concentration of a fluid component in the fluidic contentflowing through the conduit; andcharacterizing passage of the patient fluid through the conduit based onthe quantified flow of the fluidic content and on the estimatedconcentration of the fluid component in the fluidic content, at leastone of the quantifying of the flow or the estimating of theconcentration being based on the sensor data from the sensor arrangementcoupled to the conduit.

Generally, the methods and systems discussed herein are operable tocharacterize passage of a patient fluid flowing through a conduit (e.g.,among other fluidic content). Such passage of the patient fluid includesloss of the patient fluid by the patient (e.g., where the conduitconveys collected blood that was lost during surgery), gain of thepatient fluid by the patient (e.g., where the conduit conveys bloodbeing transfused into the patient), processing of the patient fluid(e.g., where the conduit conveys blood being processed through adialysis machine), or any suitable combination thereof. For clarity andbrevity, many illustrative examples discussed herein focus on situationswhere the patient fluid (e.g., blood) is lost by the patient. However,the methods and systems discussed herein are generally applicable tocharacterizing any passage of any patient fluid (e.g., urine or amnioticfluid), including passage into the patient, passage out of the patient,both (e.g., in processing the patient fluid), or neither (e.g., passagefrom one container to another container).

For example, the methods and systems described herein may be used tocharacterize fluids (e.g., bodily fluids) that are lost by a patientduring a medical procedure (e.g., labor and delivery, surgicalprocedure, etc.). For example, the methods and systems may be used totrack or otherwise estimate a quantity of fluid component (e.g., blood)lost by a patient throughout a medical procedure, and the estimate maybe updated and displayed in substantially real-time during theprocedure, at the conclusion of the procedure, or both. These methodsand systems may be used in a variety of settings, including in ahospital or clinic setting (e.g., an operating room), a military setting(e.g., a battlefield), or other suitable medical treatment settings.This information can be used to improve medical treatment of patients.For example, medical practitioners (e.g., nurses or surgeons) whoreceive this information during a surgical procedure, after the surgicalprocedure, or both, can then make appropriate decisions for treatment ofthe patient (e.g., determining whether to provide a blood transfusion tothe patient and how much blood to transfuse) based on the improvedaccuracy of this information on patient status. For example, armed withmore accurate information on the patient fluid loss or patient status,medical practitioners can better avoid delayed blood transfusions,thereby improving patient outcomes. Additionally, medical practitionerscan avoid providing unnecessary blood transfusions, which unnecessarilydeplete inventories of transfusable blood, increase operating costs andmedical bills, and increase health risks for the patient.

Estimates of the quantity of patient fluid collected may be aggregatedinto a running total or an overall estimate of the quantity of patientfluid lost by the patient during the procedure. Such estimates may, insome example embodiments, be combined with estimates of fluid collectedin batches, fluid collected cumulatively over time, or both. Forexample, a total volume of fluid, a total rate of patient fluid loss, orboth, may be estimated at any particular point during the procedure,after the procedure, or both.

In some variations, for a patient fluid of interest (e.g., blood),estimated quantities of the patient fluid from multiple sources areaggregated to generate an estimate of total loss of the patient fluid.For example, extracorporeal fluids lost by the patient may be collectedin a container, such as a canister or other fluid receptacle (e.g.,collected with a suction wand, as described below). Additionally oralternatively, fluid lost by the patient may be collected with surgicaltextiles or other absorbent items, such as surgical sponges (e.g.,laparotomy sponges), surgical dressings, surgical gauze, surgicaltowels, absorbent pads (e.g., chux pads), absorbent drapes, vaginalpacks, other textiles, other absorbent items, or any suitablecombination thereof. Textiles or absorbent items may be placed in a bag(e.g., sponge count bag) for tracking purposes, hygienic purposes, etc.Furthermore, collection of lost fluids may be performed with aspecialized container. For example, during labor and deliveryprocedures, a drape with at least one pocket (e.g., a blood collectionV-drape with a triangular pocket) may be placed under the patient forcollecting blood, amniotic fluid, urine, etc. In some variations, thequantity of fluid collected in an item, such as a surgical textile or acanister, is estimated based on a measured weight (e.g., indicatingmass) of the item when containing fluid. In some variations, thequantity of fluid collected in an item, such as a surgical textile or acanisters, is estimated using one or more of the methods or systemsdescribed in U.S. Pat. Nos. 8,792,693, 8,983,167, 9,824,441, 9,773,320,U.S. Patent Publication No. 2016/0335779, U.S. Patent Publication No.2017/0186160, U.S. Patent Publication No. 2018/01 99827, each of whichis herein incorporated by reference in its entirety.

The system and methods described herein, by way of example, for inlinefluid characterization facilitate characterization of fluidic contentwithin a conduit. For example, during a medical procedure, patientfluids may be collected in a receptacle, passed into a conduit, and thendirected into another receptacle, such as a sealed waste managementsystem. As shown in FIG. 1, a fluid retrieval device 102 (e.g., asuction wand) or other source of patient fluids collects patient fluidsfrom a surgical site, a canister, a surgical textile, or other fluidsource containing fluids to be characterized or otherwise assessed. Thecollected patient fluids may be passed via tubing into a conduit 120 andmay continue to flow into a receptacle 130 (e.g., a canister or a sealedwaste management system). In other words, the conduit 120 may be placedin fluidic communication with the fluid retrieval device 102 (or otherfluid source) and the receptacle 130. In some variations, the conduit120, the fluid retrieval device 102, or both, may be in fluidiccommunication with a vacuum source 140 (e.g., a vacuum pump of thereceptacle 130) configured to provide suction to the fluid retrievaldevice 102 for collecting fluids.

In some variations, the inline fluid characterization systems describedherein can be integrated into preexisting setups with waste managementsystems that collect patient fluids, without extensive equipmentadditions or modifications. For example, as shown generally in FIG. 1, asystem 100 for characterizing patient fluid loss by a patient includes asensor arrangement 110 couplable to the conduit 120. The sensorarrangement 110 enables inline fluid characterization and includes oneor more sensors or other measurement devices (e.g., using variousmeasurement modalities) for characterizing one or more properties of thefluid within the conduit 120. Various kinds of sensors that may beincluded in the sensor arrangement 110 are described in further detailbelow. The system 100 may further include at least one processorconfigured to characterize patient fluid loss based at least in part onthe sensor data, as further described below. Sensor data, results ofanalyzing the sensor data, or both, may be communicated by, for example,a transmission system 112 (e.g., wireless transmission system) to anysuitable external device, as further described below.

Unlike the systems and methods discussed herein, various existingsystems for analyzing fluid component concentrations in a conduit relyupon a pre-defined set of conditions to perform their analysis. Forexample, simplified flow conditions, such as laminar flow at a constantor controlled volumetric flow rate may be constrain some existingsystems. In contrast, the methods and systems described herein analyze arange of types of flow in the conduit, including laminar flow, turbulentflow, flow at varying velocity or flow rate, intermittent flow, and flowof mixed fluidic content (e.g., a mixture of patient blood, patienturine, saline, and air), all of which may occur in unpredictable fashionwhen fluids are collected through a fluid retrieval device (e.g., thefluid retrieval device 102) during a medical procedure. Furthermore,some variants of the methods and systems described herein isolateparameters of interest for a selected fluid component of the fluidiccontent in the conduit. The inline fluid characterization methods andsystems can, for example, quantify specifically a patient fluid loss(e.g., blood loss) while the patient fluid is collected and passesthrough a conduit (e.g., the conduit 120), even under unpredictable flowconditions and while mixed with other fluids such as saline and air.

Example Sensor Arrangement

Generally, as shown in FIG. 1, the system 100 includes the sensorarrangement 110, which is configured to couple to the conduit 120 forenabling inline characterization of fluidic content in the conduit 120.The sensor arrangement 110 includes at least one sensor, which may beconfigured to quantify a flow of fluidic content through the conduit,estimate a concentration of a fluid component in the patient fluid, orboth. The fluidic content includes at least one patient fluid (e.g.,blood or urine), possibly one or more other fluids (e.g., saline orair), or a combination thereof. The sensor arrangement may be at leastpartially disposed within a housing that supports the sensor components(e.g., one or more sensors, corresponding sensor electronics, a datatransmitter, a processor, etc.).

Sensor data may be stored locally, analyzed locally, or both, by one ormore processors located in or near the sensor arrangement 110. As shownin FIG. 1, the sensor data may be transmitted via a transmission system112 (e.g., a wireless or wired transmitter) configured to communicatewith a computing device 150 (e.g., a remote computing device) foranalysis. Examples of the computing device 150 are further describedbelow with respect to FIG. 18. For example, patient fluid loss may becharacterized based at least in part on quantified flow of patient fluidin the conduit 120 and the concentration of a fluid component (e.g.,blood) in the patient fluid, as further described below.

Example Housing

The sensor arrangement 110 may be at least partially disposed in ahousing that supports at least some components of the sensor arrangement110 and its accompanying electronics. The housing may also couple thesensor arrangement 110 to the conduit 120, one or more other conduits orconduit branches, or any suitable combination thereof. Alternatively, atleast some of the sensors and other components of the sensor arrangement110 may be separately coupled to the conduit 120 (e.g., outside of asingle common housing), such as being individually coupled to theconduit 120. Accordingly, in some variations, the sensor arrangement 110may be adjustable or universal in the sense that, for example, thehousing, the individual components, or any suitable combination thereof(e.g., one or more groups of grouped components), can be coupled to awide range of conduit types (e.g., without reliance on being coupled toany specific type or brand of conduit).

In some variations, the housing is configured to clamp onto the conduit120. For example, as shown in FIG. 2A, a housing 220 for a sensorarrangement 210 may include at least a first jaw 222 and a second jaw224, which together are configured to clamp onto the conduit 120. Eachjaw 222 or 224 may, for example, include at least one conduit seat 226shaped and sized to receive a segment of the conduit 120, which is shownas a conduit 250 in FIGS. 2B and 2C. For example, to receive a conduit(e.g., the conduit 250) having a circular cross-section, each jaw 222 or224 may include a semicircular conduit seat (e.g., conduit seat 226).The conduit seat 226 may be configured (e.g., shaped) to position theconduit 250 relative to one or more sensors 230 and other componentswithin the sensor arrangement 210. As shown in FIG. 2A, one or moreconduit seats 226 may be defined by cutouts on side walls of one or bothjaws 222 and 224. Alternatively, each conduit seat 226 can include anelongated, semi-circular recessed surface that extends along the lengthof the housing 220 such that, when the housing 220 clamps onto theconduit 250, the conduit seats 226 collectively form a lumen extendingalong the length of the housing. Sensors (e.g., sensor arrangements asdescribed in further detail below) may be disposed along such conduitseat surfaces.

As shown in the cross-sectional views of FIGS. 2B and 2C, first andsecond jaws 222 and 224 may be coupled by at least one joint 228 thatenables the housing to open (FIG. 2B) and close (FIG. 2C) around aconduit (e.g., the conduit 250). The joint 228 may include, for example,a hinge or other pin joint. The jaws 222 and 224 may include one joint(e.g., the joint 228) extending along the length of the housing 220 ormultiple such joints distributed along the length of the housing 220. Insome variations, the joint 228 may be spring-loaded, such as with atorsional spring, to be biased closed or biased open. Additionally oralternatively, the joint 228 may include one or more detents that biasthe jaws to be positioned at any angle among a predetermined, discreteset of angles. Although FIGS. 2B and 2C illustrate the first and secondjaws 222 and 224 to be substantially identical or symmetrical (e.g.,each forming half of the housing 220), it should be understood that thehousing 220 may include jaws of any suitable sizes relative to eachother (e.g., a deeper main jaw coupled to a shallower lid-like jaw).Furthermore, the housing 220 may include three, four, or other suitablenumbers of jaws. In other variations, the housing 220 may be configuredto wrap around the conduit 250 (e.g., with a C-shaped cross-section thatengages the conduit 250 laterally).

The housing 220 may further include one or more securing elements 229configured for securing the housing 220 in a closed position. Asecurement element 229 may include, for example, a latch, mating snapfeature, magnet, etc. In some variations, the housing 220 may includeone or more sealing elements (e.g., around the conduit seats 226, aroundthe interface between the jaws 222 and 224, etc.), such as a gasketmaterial, to fully enclose a segment of the conduit 250 within thehousing 220, in a substantially liquid-tight manner, air-tight manner,or both. Furthermore, in some variations, the housing 220, the sealingelements, or both, may be opaque (e.g., made of opaque material) to helpprevent ambient light from entering the housing 220, since such ambientlight may interfere with sensor readings within the housing 220.

The housing 220 may be sized to couple to a conduit (e.g., the conduit250) that has a predetermined diameter. For example, the housing 220 maybe configured to receive or otherwise couple to a conduit having adiameter between about 4 mm and about 20 mm, or any suitable sizeconduit. In other variations, the housing 220 may be adjustable tosecurely couple to conduits with a variety of diameters. For example, toaccommodate a range of conduit diameters without resulting in a loosecoupling around the conduit, one or more of the conduit seats 226 mayinclude a deformable surface that can compress, such that the housing220 can receive and conform to larger conduit diameters, increase involume to receive and conform to smaller conduit diameters, or both. Forexample, the deformable surface can include padding, other deformablematerial, an inflatable surface, etc. In some variations, the conduitseats 226 may be adjustable in size, such as with a mechanism similar toa leaf shutter.

FIGS. 3A and 3B illustrate another variation of a sensor arrangement 310with a conduit segment (e.g., a reusable or disposable conduit insert)that is couplable to one or more other conduit segments. As shown inFIG. 3A, the system 100 may include a conduit segment 350 that may beinserted inline between other conduit portions 360 a and 360 b. Theconduit segment 350 may include an inner volume that can be placed influidic communication with the conduit portion 360 a at an inlet end,and in fluidic communication with the conduit portion 360 b at an outletend. For example, the conduit segment 350 may be coupled inline withother conduit portions 360 a and 360 b via connectors 352 and 354, whichmay be, for example, tubing sleeve connectors, threaded connectors, orany suitable connectors. As shown in FIG. 3B, a housing 320 with sensorsmay be placed over the conduit segment 350. The housing 320 may includea lumen or other conduit receiving surface. For example, the housing 320may include a plurality of jaws (e.g., similar to the jaws 222 and 224described above with respect to FIGS. 2A-2C) or have a C-shaped crosssection that wraps around the conduit segment 350. In some variations,the conduit segment 350 is disposable and intended for single use, whilethe housing 320 is reusable. However, in other variations, the conduitsegment 350 may be designed for multiple reuses, the housing 320 maydisposable after a single use or limited number of uses, or any suitablecombination thereof.

In some variations, the housing 320 includes a pinch mechanism totemporarily reduce flow (e.g., pause or temporarily slow the flow) ofthe fluidic content through the fully or partially enclosed conduit(e.g., the conduit segment 350), so as to momentarily achieve a somewhatstable state of flow for sensor measurement to occur. The pinchmechanism may be configured to fully or partially close off the conduitat one or more axial locations along the conduit. For example, as shownin FIG. 4A, the pinch mechanism may include pincher elements 420 thatare opposed across a conduit 450 and may be actuated to move toward eachother to reduce or stop flow through the conduit 450. As anotherexample, as shown in FIG. 4B, the pinch mechanism may include cams 430,at least one having a varying radius, such that rotation of the cams 430causes the cams 430 to reduce or stop flow through the conduit 450. Insome variants, the reduction or stoppage of the flow is cyclical (e.g.,periodic) with continuous rotation of the cams 430. Alternatively, thepinch mechanism may include only one pincher element 420 or only one cam430 that is opposed a static surface (e.g., planar surface). The housingmay, in some variations, include two or more pinch mechanisms. Forexample, as shown in FIG. 5, a conduit 550 may be bifurcated into twoconduit branches 550 a and 55 b (e.g., later reunited downstream), andtwo pinch mechanisms 510 a and 510 b may be respectively coupled to theconduit branches 550 a and 550 b. When one pinch mechanism is actuatedto reduce flow in its corresponding conduit branch, a set of sensorslocated upstream of the pinch mechanism may perform measurements. Byactuating the pinch mechanisms 510 a and 510 b in an alternating manner,the system 100 may perform measurements of fluidic content in theconduit 550 without substantially restricting overall volumetric flow orsubstantially hampering upstream suction of patient fluids. In somevariations, the division of a conduit (e.g., the conduit 550) into twoor more conduit branches may be accomplished with a conduit segment,such as the inserted conduit segment 350 described above. Any of theabove-described arrangements may be substantially contained in thehousing.

Other variations of the housing may couple to the conduit in anysuitable manner. For example, in addition to clamping onto the conduit(e.g., the conduit 120, 250, 450, or 550) as described above, thehousing may be configured to slide over the conduit like a sleeve, orwrap around the conduit (e.g., in a spiral). Furthermore, as describedabove, in some variations, a disposable conduit segment and a reusablehousing unit can be coupled together and inserted inline with otherconduit portions.

Example Sensors

The sensor arrangement (e.g., the sensor arrangement 110, 210, or 310)may include one or more sensors configured to quantify a flow (e.g.,estimate a velocity of the flow, a mass flow rate of the flow, a volumeflow rate of the flow, or any suitable combination thereof), estimate afluid component concentration, or both. Generally, the one or moresensors may be positioned on or near an external surface of the conduit(e.g., the conduit 120, 250, 450, or 550). In some variations, asdescribed below, a sensor may be configured to quantify a velocity ofthe flow though the conduit. In other variations, as described below, asensor may be configured to quantify mass of fluidic content flowingthrough the conduit. Furthermore, volumetric measurements of the fluidiccontent may be derived from the quantified flow measurements. Forexample, the volumetric flow rate through the conduit may be derivedfrom the flow rate (e.g., flow velocity). In situations where the entireprofile of the conduit is filled with fluidic content, then thevolumetric flow rate is the quantified flow velocity multiplied by thecross-sectional area of the conduit. As another example, the volume offluidic content flowing through the conduit may be determined from themeasured mass of the fluidic content. In situations where the fluidiccontent is uniform, the fluidic content volume is the quantified mass ofthe fluidic content divided by a known density of the fluidic content.Other volumetric measurements may be derived based on the quantifiedflow and other information relating to the composition of the fluidiccontent (e.g., through spectroscopic analysis, as described in furtherdetail below). In some variations, one or more volumetric measurementsare used to specifically quantify patient fluid passing through theconduit, which may in turn be used to help quantify overall patientfluid loss.

Example Ultrasound Flow Sensors

In some variations, the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) includes one or more ultrasound flow sensors. Forexample, as shown in FIG. 6A, the sensor arrangement may include atleast one ultrasound Doppler flow meter 620 that is located adjacent orproximate a conduit 650 in which fluidic content is flowing. Theultrasound Doppler meter 620 may include transducer including atransmitter (Tx) configured to emit a beam of ultrasonic waves into theconduit 650 and a receiver (Rx) configured to detect ultrasonic wavesthat are reflected by the fluidic content in the conduit 650. Thefrequency shift of the reflected ultrasonic waves may be analyzed todetermine relative motion among the transmitter, the fluidic content,and the receiver. With the transmitter and receiver fixed relative tothe conduit 650, the measured relative motion may be correlated to flowrate of the fluidic content in the conduit 650.

Generally, flow rate can be estimated based on Equation 1 below:

$\begin{matrix}{V = {c\frac{f_{r} - f_{t}}{2f_{t}{\cos(\Phi)}}}} & (1)\end{matrix}$

where V=flow rate, c=speed of sound in the fluidic content,f_(t)=transmitted frequency, f_(r)=received frequency, and Φ=relativeangle between the transmitted ultrasonic beam and the fluid flowdirection. Some of these parameters, such as speed of sound in thefluidic content, depends on the composition (e.g., fluid components) ofthe fluidic content in the conduit 650. Accordingly, in some variations,varying values of c for speed of sound may be used in Equation 1. Forexample, the composition of the fluidic content in the conduit 650 maybe determined separately (e.g., through spectroscopic analysis, asdescribed below) such that relative amounts of various fluid components(e.g., blood, saline, air, etc.) are determined. A representative valueof c for use in Equation 1 may be generated based on the composition ofthe fluidic content. For example, the representative value of c may be aweighted average of c for the various fluid components in the conduit650. As another example, the representative value of c may be determinedbased on a look-up table of speeds of speed for different types offluid, different compositions (e.g., mixtures) of various fluidcomponents, or any suitable combination thereof. In other variations, afixed representative value of c (e.g., an average value of speeds ofsound for all expected fluidic components of the fluidic content) may beused in Equation 1.

In another variation, as shown in FIG. 6B, the sensor arrangement (e.g.,the sensor arrangement 110, 210, or 310) includes one or moretime-of-flight (ToF) ultrasound flow meters. For example, a ToF flowmeter may include two transducers, including an upstream transducer 630and a downstream transducer 640 located on opposite sides of the conduit650. The upstream transducer 630 may include a transmitter configured toemit a beam of ultrasonic waves toward a receiver in the downstreamtransducer 640 (e.g., generally in the direction of flow). Conversely,the downstream transducer 640 may include a transmitter configured toemit a second beam of ultrasonic waves toward a receiver in the upstreamtransducer 630 (e.g., generally against the direction of flow). In othervariations, the upstream and downstream transducers are located on thesame side of the conduit 650 but direct ultrasound waves toward anacoustically reflective (e.g., acousto-reflective) surface on theopposite side of the conduit 650. The difference in travel times of theultrasonic waves propagating with and against the direction of flow maybe analyzed to determine the flow rate of the fluidic content in theconduit 650.

Generally, flow rate along the sound path of the ultrasonic beams (whichmay be an approximation for flow rate in the flow direction) can beestimated based on Equation 2 below:

$\begin{matrix}{V = {\frac{L}{2\;{\cos(\Phi)}}\frac{t_{up} - t_{down}}{t_{up}t_{down}}}} & (2)\end{matrix}$

where V=flow rate, L=distance between the upstream and downstreamtransducers, t_(up)=transit time in upstream direction, t_(down)=transittime in downstream direction, and Φ=relative angle between thetransmitted ultrasonic beams and the fluid flow direction.

In some variations, the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) includes multiple kinds of ultrasound flow meters. Forexample, the sensor arrangement may include at least one ultrasoundDoppler flow meter (e.g., similar to the flow meter described above withrespect to FIG. 6A) and at least one ToF flow meter (e.g., similar tothe flow meter described above with respect to FIG. 6B). The combinationof both kinds of ultrasound flow meters may, for example, expand therange of conditions under which flow rate can be measured. Generally, anultrasound Doppler flow meter can detect and analyze ultrasound wavesthat reflect off particles or other distinct features in the fluidiccontent flowing in the conduit 650 (e.g., blood cells, air bubbles,overall turbulence of the fluid itself, or any suitable combinationthereof). Under certain conditions (e.g., pure laminar flow of salinethat lacks particles), the ultrasound Doppler flow meter may provideless accurate measurements. On the other hand, a ToF flow meter candetect and analyze transmitted ultrasound waves in laminar flow withoutparticles. Accordingly, the combination of a Doppler flow meter and aToF flow meter may enable accurate flow rate measurements under a widerrange of operating conditions, including both turbulent and laminarflow, and flow of fluids with and without particles.

Example Optical Flow Sensors

In yet other variations, the sensor arrangement (e.g., the sensorarrangement 110, 210, or 310) includes one or more optical sensorsconfigured to measure flow rate of the fluidic content in the conduit(e.g., the conduit 120, 250, 450, 550, or 650). For example, as shown inFIG. 7A, the sensor arrangement may include an emitter array 710 and adetector array 720 located generally on opposite sides of a conduit 750.The emitter array 710 may include a series of optical emitters (e.g.,LEDs), and the detector array 720 may include a series of opticaldetectors (e.g., CMOS sensors), with opposing optical emitters andoptical detectors forming a series of emitter-detector pairs. Eachoptical emitter is configured to emit light toward its correspondingoptical detector, such that the emitted light is transmitted through thefluidic content in the conduit 750. In some variations, at least someoptical emitters may be configured to emit a predetermined wavelength oflight based on absorption characteristics of a substance of interest(e.g., blood or hemoglobin). For example, at least some optical emittersmay be configured to emit light having a wavelength of around 532 nm,such that the emitted light is optimized for maximal absorption byhemoglobin in the fluidic content in the conduit 750. Additionally oralternatively, in some variations, at least some optical emitters may beconfigured to emit a broad range of wavelengths (e.g., white lighthaving a broad spectrum).

The signal strength output by an optical detector may be used todistinguish between different fluid components of the fluidic contentpassing through the conduit 750 between the optical detector and itscorresponding optical emitter. In the example shown in FIG. 7A, threeboluses B1, B2, and B3 of a patient fluid (e.g., blood) are depictedflowing through the conduit 750 between optical emitters and theircorresponding optical detectors (e.g., between emitter LED1 andcorresponding detector PD1). The boluses B1, B2, and B3 are interspersedwith air. As shown in FIG. 7B, the intensity I of the signal from PD1varies over time (t) as the boluses and interspersed air pass throughthe conduit 750. Specifically, lower signal intensity corresponds topassage of patient fluid (e.g., boluses B1, B2, and B3) due to thepatient fluid's absorption of the emitted light, and higher signalintensity corresponds to passage of air. Accordingly, patient fluid,such as blood, can be distinguished from air based on the signalintensity output by one or more optical detectors. Furthermore, in somevariations, the type of non-air fluid (e.g., blood, saline, urine, etc.)may be identified based on the signal intensity from one or more opticaldetectors, since different fluids may absorb different amounts of light.

As shown in FIG. 7A, at least some of the emitter-detector pairs areaxially spaced apart along the conduit 750. For example, a secondemitter-detector pair may be located downstream of a firstemitter-detector pair. Specifically, the first emitter-detector pair mayinclude a first optical emitter LED1 opposite a first photodetector PD1that is configured to detect light emitted by the first optical emitterLED1. Similarly, the second emitter-detector pair may include a secondoptical emitter LED2 opposite a second photodetector PD2 that isconfigured to detect light emitted by the second optical emitter LED2.Since the second emitter-detector pair is located downstream of thefirst emitter-detector pair, the second photodetector PD2 detects asegment of fluidic content after the first photodetector PD1 detects thesame segment. This temporal offset between the patient fluid detectionevents in the first and second photodetector PD1 and PD2 signals can becorrelated to an estimated flow velocity between the first and secondemitter-detector pairs by Equation 3:

$\begin{matrix}{V = \frac{L}{\Delta\; t}} & (3)\end{matrix}$

where V=flow velocity, L=distance between the first and secondemitter-detector pairs, and Δt=temporal offset between patient fluiddetection events for the first and second optical detectors (e.g.,photodetectors PD1 and PD2). Thus, cross-correlation between thephotodetectors PD1 and PD2 can be used to measure flow velocity. In somevariations, the addition of a third, fourth, or more axially-spacedemitter-detector pairs may be used for supplemental or added accuracy orprecision (e.g., by averaging cross-correlations of different sets ofemitter-detector pairs), for verifying the flow velocity measurement(e.g., by checking for variation or redundancy in the time offset), orfor both. The volumetric flow rate can then be estimated by, forexample, multiplying the flow velocity V with the cross-sectional area Aof the fluid in the conduit.

As shown in FIG. 7A in dashed lines, emitted light may undergorefraction when being transmitted through certain media. To account forthis refraction and be able to detect refracted light whose incidentlocation on a detector is not directly opposite its emission location,an optical detector in an emitter-detector pair may have a detectionregion that is wide enough, long enough, or both, to capture refractedlight. For example, at least some of the optical detectors may be orfunction as an area sensor configured to detect light falling onto atwo-dimensional (e.g., rectangular) region, or may be a line sensorconfigured to detect a light falling onto a one-dimensional (e.g.,linear) region. Additionally, in some variations, spectroscopicproperties of the fluidic content (e.g., determined by spectroscopicanalysis, as described below) may be used to quantify the refractivenature of the fluidic content, such as through a look-up table or anequation based on weighted values of the spectroscopic properties. Giventhat L in Equation 3 above may be affected by the refraction of thefluid, a refractive value representing the refractive nature of thefluidic content can then be used to dynamically adjust the value of L toaccount for different compositions of fluid passing through the conduit750. Furthermore, in some variations, the type of fluid (e.g., blood,saline, urine, air, etc.) may be identified based on the incidentlocation of detected light relative to the emission location, sincedifferent fluids generally have different indices of refraction.

In some variations, the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) includes emitter-detector pairs arranged at aplurality of circumferential locations around the conduit (e.g., theconduit 120, 250, 450, 550, 650, or 750). For example, as shown in FIG.8A, a first emitter-detector pair (LED11, PD11) is circumferentiallyoffset from a second emitter-detector pair (LED12, PD12), though thefirst and second emitter-detector pairs are located at the sameapproximate axial location. In some variations, to reduce interferencebetween emitter-detector pairs located at the same approximate axiallocation, each emitter-detector pair at that axial location may operateon a distinct corresponding wavelength. For example, the optical emitterLED11 may be configured to emit light only at a first wavelength, andits corresponding photodetector PD11 may be configured to detect lightonly at the first wavelength. Similarly, the optical emitter LED12 maybe configured to emit light at a second wavelength different from thefirst wavelength, and the photodetector PD12 may be configured to detectonly light at the second wavelength.

In some variations, interference between axially-alignedemitter-detector pairs may additionally or alternatively be reduced byalternating the optical emitters and optical detectors. For example, asshown in FIG. 9, a set of three emitter-detector pairs (LED11, PD11),(LED12, PD12), and (LED13, PD13) may be arranged in alternating fashion(e.g., with no two optical emitters immediately adjacent to each otherand no two optical detectors immediately adjacent to each other).Accordingly, the photodetector PD11 in the first emitter-detector pairis unlikely to receive light from either the optical emitter LED12 orthe optical emitter LED13, respectively from the second and thirdemitter-detector pairs, for example. Thus, each optical detector willreceive substantially only the light emitted from its correspondingoptical emitter.

FIG. 8B illustrates a cross-section of the sensor arrangement depictedin FIG. 8A, taken at the axial location of the first and secondemitter-detector pairs. Circumferentially distributed emitter-detectorpairs may enable measurement of the flow rate when the cross-sectionalarea of a conduit 850 is not completely filled with a non-air fluid. Forexample, as shown in FIG. 8B, the conduit 850 may be only partiallyfilled with a bolus B of a patient fluid (e.g., blood). In thisillustrative example, the bolus B does not traverse the entire distancebetween a first optical emitter LED11 and its corresponding firstphotodetector PD11, and the signal from that first photodetector PD11may not be sufficient to reliably indicate the presence of the bolus B.Thus, the first emitter-detector pair with that first photodetector PD11may not be sufficient to facilitate reliable flow rate measurements asdescribed above with respect to FIGS. 7 A and 7B. However, as shown inFIG. 8B, the bolus B does traverse the entire distance between a secondoptical emitter LED12 and its corresponding second photodetector PD12,such that the signal from that second photodetector PD12 may be used forflow rate measurements (e.g., alone or together with output from atleast one of the downstream emitter-detector pairs (LED21, PD21) and(LED22, PD22)). Thus, such circumferentially-arranged emitter-detectorpairs may enable measurement of the flow rate, even if the conduit 850is only partially filled (e.g., half full). It should be understood thatmore than two emitter-detector pairs (e.g., three, four, five, or anysuitable number) may be arranged circumferentially around the conduit850 to provide more sensitivity to the presence of non-air fluid in theconduit.

Furthermore, in some variations, the circumferentially arranged opticaldetectors can inform on the distribution of fluid, the amount of fluid,or both, at their shared axial location on the conduit 850. For example,with reference to FIG. 8B, because the bolus B absorbs less light fromthe first optical emitter LED11 and absorbs more of the light from thesecond optical emitter LED12, the signal intensity from the firstphotodetector PD11 is higher than the signal intensity from the secondphotodetector PD12. Thus, the relative signal intensities for the firstand second photodetectors PD11 and PD12 can indicate the distributionand amount of the patient fluid at the axial location of the first andsecond photodetectors PD11 and PD12. For example, twoorthogonally-arranged emitter-detector pairs can detect whether theconduit is half full or entirely full. More emitter-detector pairs maybe arranged circumferentially around the conduit 850 (e.g., at the sameaxial location on the conduit 850) to provide more resolution in thecross-sectional area of the conduit 850 that contains the fluid beingmeasured.

As described above, the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) may include optical emitter-detector pairs arranged ata plurality of axial locations along the conduit (e.g., the conduit 120,250, 450, 550, 650, 750, or 850), at a plurality of circumferentiallocations around the conduit, or both. For example, as shown in FIG. 8A,at least some of a plurality of emitter-detector pairs may be arrangedin a series of axially spaced-apart rings. As another example, at leastsome of a plurality of emitter-detector pairs may be arranged helicallyaround the conduit. A helical arrangement may, for example, help avoidoptical interference between adjacent emitter-detector pairs.

Example Thermal Mass Flow Sensor

In some variations, as shown in FIG. 10, the sensor arrangement (e.g.,the sensor arrangement 110, 210, or 310) includes a thermal mass flowsensor 1010 configured to quantify the mass of fluidic content flowingthrough a conduit 1050. For example, estimating a thermal mass flow ofthe fluidic content can include introducing a known amount of heat intothe flow of fluidic content and measuring the associated (e.g.,corresponding) temperature change in the fluidic content. This may beperformed, for example, by maintaining a probe at a constant temperatureand measuring the energy consumed in doing so. As another example,estimating a thermal mass flow of the fluidic content can includeintroducing a known amount of heat into the flow of fluidic content andmeasuring the change in temperature of the fluidic content at some pointdownstream.

For example, as shown in FIG. 10, the sensor arrangement (e.g., thesensor arrangement 110, 210, or 310) may include at least a firsttemperature sensor TS1 and a second temperature sensor TS2 locateddownstream of the first temperature sensor TS1. The first and secondtemperature sensors TS1 and TS2 may, for example, be based onmicroelectromechanical systems (MEMS). One or more heat sources 1012,such as a heating coil or other suitable heating element, may introducea known amount of heat energy into the fluidic contents of the conduit1050 (e.g., through the wall of the conduit 1050) between the first andsecond temperature sensors TS1 and TS2. The difference in temperaturemeasurements by the first and second temperature sensors TS1 and TS2 asa result of the heating of the fluidic contents in the conduit 1050 canbe correlated to a measurement of mass flow, according generally toEquation 4:

$\begin{matrix}{m = \frac{Kq}{C_{p}\left( {T_{2} - T_{1}} \right)}} & (4)\end{matrix}$

where m=mass flow, K=a conduit constant, C_(p)=specific heat of fluidiccontents of the conduit 1050, T₂=temperature measurement by the secondtemperature sensor TS2, and T₁=temperature measurement by the firsttemperature sensor TS1. The value of K may be known or looked up basedon properties of the conduit 1050 (e.g., tubing material, tubing wallthickness, tubing diameter, etc.), or may be determined based onprevious experimental data, for example. The value of K may be manuallyentered (e.g., by a user), recorded in and received from acomputer-readable storage medium, automatically recognized by the system100, such as through optical character recognition of a label on theconduit 1050 or another suitable reference code, or any suitablecombination thereof. In some variations, the value of K may be derivedfrom an optical or electrical resistance sensor, where the sensor outputcan be used to determine the K value (e.g., via a look-up table, anequation, etc.). Multiple methods for determining the K value can becombined (e.g., averaged) or used for redundancy purposes. The specificheat of fluidic contents of the conduit 1050 may be a constant valuethat is known or assumed as representative of a known fluid passingthrough the conduit 1050. However, in some variations, the value ofC_(p) may vary depending on other factors. For example, the value ofC_(p) may depend on the composition of the fluidic content (e.g., byweight averaging respective specific heat values for each fluidcomponent), which may be determined with spectroscopic analysis, forexample, as described below. Once composition of the fluidic content isdetermined, a look-up table, heuristically-derived equation, othersuitable tool, or any suitable combination thereof, may be used todetermine a suitable value of C_(p) in Equation 4.

In some variations, one or more heat sources 1012 (e.g., heating coilsor other heating elements) may be positioned on or adjacent to anexternal surface of the conduit 1050. For example, a heating element maybe mounted in a housing (e.g., the housing 220) of the sensorarrangement (e.g., the sensor arrangement 110, 210, or 310), such thatwhen the conduit 1050 is received in the housing, the conduit 1050 isself-aligning and contacts the heating element. In other variations, oneor more heating elements may be integrated in the material of theconduit 1050 so as to more directly heat the fluidic contents in theconduit 1050.

Although two temperature sensors (e.g., the first and second temperaturesensors TS1 and TS2) are depicted in FIG. 10, it should be understoodthat three, four, five, or more temperature sensors may be included tomeasure thermal mass flow. For example, an array of temperature sensors(e.g., with one or more heating elements) may be arranged at a pluralityof axial locations, at a plurality of circumferential locations (e.g.,in a series of rings, in helical fashion, etc.), or both. Likearrangements of optical flow sensors as described above, the combinationof axial and circumferential arrangements of thermal mass flow sensorsmay enable averaging of thermal mass flow results, verifying of thermalmass flow results, or both. Furthermore, such combined arrangements canprovide thermal mass flow measurements under a greater variety of fillconditions (e.g., when the conduit 1050 is partially filled or fullyfilled).

In some variations, any of the optical flow sensor arrangements and anyof the thermal mass flow sensor arrangement described above may becombined to enable quantification of flow across a wider range ofconditions. For example, optical flow sensors may be more accuratequantifying flow (e.g., estimating the flow rate) within the conduit(e.g., the conduit 120, 250, 450, 550, 650, 750, 850, or 1050), when theflow is intermittent (e.g., includes boluses of liquid interspersed withair), while thermal mass flow sensors may be more accurate quantifyingflow (e.g., estimating the thermal mass flow) when the flow iscontinuous. Accordingly, use of optical flow sensors and thermal massflow sensors together may enable accurate quantification of flow in aconduit across a wider range of flow conditions (e.g., both intermittentand continuous flow) in the conduit, such that the system 100 is evenmore robust despite unpredictable flow conditions. Furthermore, in somevariations, the fluidic content may not need to be preprocessed to havepredetermined characteristics (e.g., moving in laminar flow) in orderfor the system 100 to accurately quantify patient fluid loss.

Example Color Image Flow Sensor

In yet other variations, the sensor arrangement (e.g., the sensorarrangement 110, 210, or 310) includes one or more image sensorsconfigured to quantify flow, estimate composition or componentconcentration of the fluidic content in the conduit (e.g., the conduit120, 250, 450, 550, 650, 750, 850, or 1050), or both. For example, anoptical image sensor (e.g., CCD, CMOS, etc.) may capture a color opticaldigital image with red, green, and blue (RGB) color components for thepixels (e.g., along with other suitable optical components). Forexample, the image sensor may be a single image sensor paired withsuitable corresponding optics, filters (e.g., color filter arrays suchas a Bayer pattern filter), or any suitable combination thereof. Asanother example, the sensor arrangement may include multiple imagesensors paired with suitable corresponding optics, such as at least oneprism or diffractive surface to divide white light into separate colorchannels, each of which is detected by a respective image sensor (e.g.,with an appropriate optical filter corresponding to a color channel).However, the sensor arrangement may include any suitable image sensorsand other optical components to enable the sensor arrangement togenerate images of the conduit and its fluidic content.

A machine learning algorithm (e.g., a neural network or other suitablealgorithm) may be applied to the image data to distinguish betweendifferent kinds of flow such as laminar, turbulent, and air in theconduit (e.g., the conduit 120, 250, 450, 550, 650, 750, 850, or 1050).For example, a machine configured in accordance with such a machinelearning algorithm may be trained to classify flow using experimentallyderived training data. Images of the fluidic content of the conduit maythen be provided as inputs to the trained machine (e.g., trained byinputting the training data into the machine learning algorithm). Insome variations, the trained machine can analyze color component values(e.g., RGB, CYMK, or other suitable color space) to determine areas ofthe conduit likely to be air, laminar flow, turbulent flow, etc. Byidentifying features in the image of the fluidic content, the trainedmachine may additionally or alternatively quantify aspects of the flow(e.g., flow rate) within the conduit. Other example methods ofcharacterizing fluid in a conduit by analyzing color images of the fluidare described in U.S. Patent Publication No. 2016/0335779, which wasincorporated by reference above.

Additionally or alternatively, color component information may becorrelated to composition of the fluidic content. For example, theconcentration of a fluid component of the fluidic content can bedetermined from color component pixel values using methods described inU.S. Pat. No. 8,792,693, which was incorporated by reference above. Insome variations, the same one or more color image sensors may be used toquantify flow of fluidic content (e.g., as described above) and estimatean aspect of the composition of the fluidic content. In some variations,a machine learning algorithm (e.g., a neural network or other suitablealgorithm) may be applied to the image data to identify constituentfluid components of the fluidic content in the conduit (e.g., theconduit 120, 250, 450, 550, 650, 750, 850, or 1050). For example, amachine configured in accordance with such a machine learning algorithmmay be trained using experimentally-derived training data. Images of thefluidic content of the conduit may then be provided as inputs to thetrained machine (e.g., trained by inputting the training data into themachine learning algorithm). In some variations, the trained machine cananalyze color component values (e.g., RGB, CYMK, other suitable colorspace) to determine color-related pixel values that are correlated tovarious fluid components (e.g., with the intensity of the red channelfor a pixel correlated to blood). By identifying features in the imageof fluidic content, the trained machine can identify information aboutrelative amounts of fluid types, relative concentrations of fluidcomponents, etc. within the conduit.

Example Multispectral Image Sensor

In some variations, the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) further includes one or more sensors configured toestimate the concentration of one or more fluid components of thefluidic content, other composition of the fluidic content of the conduit(e.g., the conduit 120, 250, 450, 550, 650, 750, 850, or 1050), or both.For example, as shown in FIG. 11, the sensor arrangement may include amultispectral imaging arrangement 1110 that includes a multispectralemitter array LEDc configured to emit a range of wavelengths of light,and a multispectral detector array PDc configured to detect a range ofwavelengths of light reflected by the fluidic content in a conduit 1150.For example, the multispectral emitter array LEDc, which may includeLEDs or other suitable optical emitters, may be configured to emit lightgenerally in the visible spectrum, the near infrared spectrum, or both(e.g., emit light generally having wavelengths between 400 nm and 900nm). The multispectral emitter array LEDc may include multiple LEDs,each emitting light of a subset of the range of wavelengths (e.g., 400nm-450 nm, 450 nm-500 nm, etc.), such that collectively the emitterarray LEDc emits the suitable multispectral range. The multispectraldetector array PDc, which may include CMOS sensors or ocher suitableimage sensors, may also be configured to detect light generally in thevisible spectrum, the infrared spectrum, or both. For example, themultispectral detector array PDc may include multiple CMOS sensors, eachsensor having a corresponding filter, such that each CMOS sensor detectsonly wavelengths of light permitted by the filter. In an exampleembodiment, the multispectral imaging arrangement 1110 includes betweenabout 10 and about 100 LEDs (e.g., in the emitter array LEDc) andbetween about 10 and 100 CMOS sensors (e.g., in the detector array PDc).Each LED may emit a respective discrete range of wavelengths of light,and may have a corresponding CMOS sensor that detects the same range ofwavelengths of light.

Various aspects of the composition of the fluidic content can bemeasured by analyzing the detector signals from the multispectralimaging arrangement 1110 in spectroscopic analysis. Specifically, sincedifferent kinds of matter absorb or reflect different wavelengths oflight, the spectral pattern measured by the multispectral detector arrayPDc can be analyzed to characterize the composition of the fluidiccontent. In some variations, the relative amounts (e.g., concentrations)of fluid types present (e.g., blood, urine, saline, etc.) can bedetermined by analyzing the signals from the detector array PDc. Forexample, blood generally has an absorption spectra peak of about 530 nm.Accordingly, signal from a sensor in the multispectral detector arrayPDc that detects light at 530 run may be correlated to the amount ofblood that is in the conduit 1150. As another example, urine generallyhas an absorption spectra peak of about 430 nm, such that signal from asensor in the multispectral detector array PDc that detects light at 430nm may be correlated to the amount of urine that is in the conduit 1150.In some variations, the signals from the multispectral detector arrayPDc may be processed through spectroscopic analysis to determine theconcentration of a fluid component (e.g., hemoglobin) among other fluidcomponents in the fluidic content. Furthermore, in some variations, thesame sensor signals may be processed through spectroscopic analysis todetermine level of hemolysis, so as to obtain a measure of amount ofwhole blood relative to hemolyzed blood.

For fluid in the conduit 1150 having multiple fluidic components, theunscattered absorbance for each of the total of n fluidic components(n=1, 2, 3, . . . ) may be summed to provide a total absorbance ΣA thatis directly measured by the multispectral imaging arrangement 1110 atdifferent wavelengths, for example according to Equation 5:

ΣA(λ)=Σ_(i=1) ^(n)∈_(i)(λ)c _(i) d _(i)  (5)

where ∈_(i)(λ) is the absorptivity (known for many substances), c_(i) isthe concentration (to be determined), and d_(i) is the optical pathlength in each fluid component in the conduit 1150. By measuring thetotal absorbance at n or more wavelengths of light, the system 100 candetermine the concentration of each of the constituent fluid componentsof the fluidic content in the conduit 1150, such as according toEquations 6:

$\begin{matrix}{{{{{ɛ_{1}\left( \lambda_{1} \right)}c_{1}d_{1}} + {{ɛ_{2}\left( \lambda_{1} \right)}c_{2}d_{2}} + \cdots\mspace{14mu} + {{ɛ_{n}\left( \lambda_{1} \right)}c_{n}d_{n}}} = {\sum{A\left( \lambda_{1} \right)}}}{{{{ɛ_{1}\left( \lambda_{2} \right)}c_{1}d_{1}} + {{ɛ_{2}\left( \lambda_{2} \right)}c_{2}d_{2}} + \cdots\mspace{14mu} + {{ɛ_{n}\left( \lambda_{2} \right)}c_{n}d_{n}}} = {\sum{A\left( \lambda_{2} \right)}}}\vdots{{{{ɛ_{1}\left( \lambda_{n} \right)}c_{1}d_{1}} + {{ɛ_{2}\left( \lambda_{n} \right)}c_{2}d_{2}} + \cdots\mspace{14mu} + {{ɛ_{n}\left( \lambda_{n} \right)}c_{n}d_{n}}} = {\sum{A\left( \lambda_{n} \right)}}}} & (6)\end{matrix}$

Thus, all the fluidic component concentrations can be estimated byputting the above Equations 6 in matrix form and performing mathematicaloperations on the matrix (e.g., by obtaining a least-square estimate).

Other variations of the sensor arrangement (e.g., the sensor arrangement110, 210, or 310) include any suitable combination of theabove-described sensors (e.g., ultrasound, optical, thermal, colorimage, and multispectral image), which may be used to quantify flow offluidic content through a conduit (e.g., the conduit 120, 250, 450, 550,650, 750, 850, 1050, or 1150), estimate composition of the fluidiccontent (e.g., estimate a fluidic component concentration), or both. Forexample, analysis of data from multiple sensors can be averaged orotherwise combined in a fusion algorithm to improve accuracy.Furthermore, use of multiple sensors can provide redundancy, such thatfailure of one sensor, for example, might not render the system 100inoperable. As another example, as described above in a few examples,multiple measuring modalities can improve sensitivity, reliability, orboth, of the measurements across a wider range of flow conditions (e.g.,turbulent or laminar, continuous or intermittent, mixed fluid types,etc.). Furthermore, in some variations, a single sensor arrangement(e.g., a color image sensor arrangement) may provide sufficient data toenable both quantifying flow and estimating concentration of one or morefluid components, as described above.

Example Processor and Example Memory Components

Generally, the system 100 may include one or more processors configuredto execute instructions that are stored in memory, such that, when theone or more processors execute the instructions, the one or moreprocessors performs aspects of the methods described herein. Forexample, the one or more processors may be configured to quantify flowof fluidic content through a conduit (e.g., the conduit 120, 250, 450,550, 650, 750, 850, 1050, or 1150), estimate a concentration of a fluidcomponent in a patient fluid, characterize patient fluid loss based atleast in part on the quantified flow and the concentration of the fluidcomponent, or any suitable combination thereof. Other aspects of dataanalysis may additionally be performed by the one or more processors.

In an example variation, the system 100 estimates the amount of patientblood that has been collected and passed through the conduit, based atleast in part on the sensor measurement described above. For example,the mass of hemoglobin that has passed through the conduit over ameasurement period of time can be estimated by multiplying the bloodvolumetric flow rate through the conduit to the concentration ofhemoglobin in the conduit, as measured during the measurement period oftime. The volumetric flow rate of blood can be determined based onoverall flow rate (e.g., as quantified by the one or more sensorsdescribed above) and the relative amount of blood compared to otherfluid types in the conduit (e.g., as measured with spectroscopic oroptical analysis as described above). The concentration of hemoglobincan be measured with spectroscopic or optical analysis, as describedabove. Furthermore, patient blood loss can be estimated by dividing theestimated hemoglobin mass loss by a known serum-derived patienthemoglobin value. The estimated patient blood loss over the measurementperiod of time can be aggregated over multiple measurement periods oftime as a running patient blood loss total, aggregated with otherestimations based on other fluid collection sources such as surgicaltextiles, as described above, or both. Such patient blood loss metricscan be indicated to a user, such as through a display or audio device,as described in further detail below. In some variations, the estimatedhemoglobin mass itself may be a metric characterizing patient fluidloss. Furthermore, in some variations, any of the methods describedherein may be applied to determine the concentration and flow rate ofother bodily fluids (e.g., urine, amniotic fluid, spinal fluid, bile)through a conduit. The measured values can be aggregated over multiplemeasurement periods of time as a running fluid loss total, aggregatedwith other estimations based on other fluid collection sources, asdescribed above, or both.

Referring back to FIG. 1, one or more processors 154 may, for example belocated in a computing device 150, which may be remote computing devicerelative to the sensor arrangement 110. Examples of the one or moreprocessors 154 are further described below with respect to FIG. 18. Thecomputing device 150 may be or include a handheld or mobile device(e.g., a tablet computer, laptop computer, mobile smartphone, etc.). Theinstructions discussed above may be executed, for example, bycomputer-executable components integrated with an application, applet,host, server, network, website, communication service, communicationinterface, hardware elements, firmware elements, software elements,wristband, smartphone, or any suitable combination thereof. In someexample embodiments, the instructions may be performed at least in partby one or more processors that are separate from the computing device150 (e.g., on-site in the operating room or remotely outside theoperating room), such as a cloud-based computer system, a mainframecomputer system, a grid-computer system, or other suitable computersystem.

The instructions may be stored on memory 152 of the computing device 150or other computer-readable medium such as RAMs, ROMs, flash memory,EEPROMs, optical devices (e.g., CD or DVD), hard drives, floppy drives,or any suitable storage device. Examples of the memory 152 are furtherdescribed below with respect to FIG. 18. Additionally or alternatively,one or more processors may be located in or near the sensor arrangement100 for local analysis.

Furthermore, sensor data, results of any analysis performed by a localprocessor at the sensor arrangement 110, or both, may be communicated ina wired or wireless connection (e.g., via WiFi, Bluetooth, Zigbee,Airdrop, etc.) with a remote computing device, such as the computingdevice 150 shown in FIG. 1. In some variations, one or more local memorycomponents, similar to those described above, may be included in thesensor arrangement 110 in order to store a queue or backlog of suchdata. This queue or backlog may, for example, provide a buffer of storeddata to avoid loss of data in the event that data transfer to thecomputing device 150 is interrupted or otherwise fails.

Example User Interactive Components

In some variations, as shown in FIG. 1, the system 100 may furtherinclude one or more displays 148 configured to display data, an analysisof the data (e.g., estimation of patient fluid loss) to a user, or both.The display 148 may include a screen on a handheld or mobile device(e.g., as part of the same computing device 150 as the one or moreprocessors 154 described above), a computer monitor, a televisionscreen, a projector screen, or other suitable display. In somevariations, the display 148 is configured to display a user interface(e.g., a graphical user interface) that enables the user to inputinformation (e.g., a serum-derived patient hemoglobin value, or conduittube information, such as brand, material, size, wall thickness, etc.,which may be used to calibrate the system 100 to a specific conduittype), select display options (e.g., font, color, language, etc.),display content (e.g., patient information, fluid-related information,alerts, etc.), or any suitable combination thereof. In such variations,the display 148 may be user-interactive and include a resistive orcapacitive touch screen that is responsive to skin, a stylus, or otheruser contact. In other variations, the display 148 may beuser-interactive via a cursor controlled by a mouse, keyboard, or othersuitable input device.

In some variations, the system 100 may additionally or alternativelyinclude an audio system 156 configured to communicate information (e.g.,fluid-related information, alerts, etc.) to a user. For example, thedisplay 148, the audio system 156, or both, may provide alerts or alarmsupon the estimated quantity of trend of fluid loss meeting a threshold,which may be useful to prompt certain actions in response, such asproviding a blood transfusion to the patient.

The descriptions herein, for purposes of explanation, use specificnomenclature to provide a thorough understanding of the present subjectmatter. However, it will be apparent to one skilled in the art thatspecific details are not required in order to practice the presentsubject matter. Thus, the descriptions of specific example embodiments(e.g., variants or variations) of the present subject matter are forpurposes of illustration. They are not intended to be exhaustive or tolimit the subject matter to the precise forms disclosed, as manymodifications and variations are possible in view of the aboveteachings. The example embodiments described herein explain theinnovations involved and their practical applications, and they therebyenable others skilled in the art to utilize the present subject matterand various example embodiments thereof with various modifications assuited to the particular use contemplated.

Example System Modules

FIG. 12 is a flowchart illustrating operation of the system 100 inperforming a method 1200 of characterizing fluidic contents flowingthrough a conduit (e.g., the conduit 120, 250, 450, 550, 650, 750, 850,1050, or 1150), according to some example embodiments. In such exampleembodiments, images (e.g., video images) of the fluidic content flowingthrough the conduit are captured by an optical sensor (e.g., a camera)and are inputted into an optical mass flow algorithm 1210, which isshown as including fluid motion tracking followed by fluid massestimation. The output of the optical mass flow algorithm 1210 isprocessed by an optical mass flow calibration operation 1220, and theresultant output is provided to a sensor combining operation 1240.

On a separate path, the images of the fluidic content are also providedas input to a video classifier 1230, which may be configured todistinguish laminar flows from non-laminar flows. The output of thevideo classifier 1230 is also provided to the sensor combining operation1240. In addition, the output of the video classifier 1230 may serve asinput to an optical hemoglobin concentration detector 1250, whose outputfeeds the sensor combining operation 1240.

Furthermore, sensor data from an ultrasound sensor is accessed andprovided as input to an ultrasound calibration operation 1215, and theresultant output is fed as input to the optical hemoglobin concentrationdetector 1250. As noted above, the output from the optical hemoglobinconcentration detector 1250 is provided to the sensor combiningoperation 1240.

The sensor combining operation 1240, thus, combines its inputs to obtainthe concentration of the fluid component (e.g., hemoglobin) of thepatient fluid (e.g., blood) included in the fluidic contents flowingthrough the conduit. Moreover, with the combined inputs based onmultiple types of sensor data (e.g., video data and ultrasonic data),the sensor combining operation 1240 outputs accurate results under awide variety of flow conditions for the fluidic content in the conduit.Examples of such conditions include regions of continuous flow, regionsof non-continuous flow (e.g., intermittent flow or otherwise irregularflow), regions of laminar flow, and regions of turbulent flow, as wellas any suitable combinations thereof.

FIG. 13 is a diagram illustrating motion tracking of fluidic content,according to some example embodiments. As shown in FIG. 13, when fluidiccontent passes through the conduit, the attenuation of light due toabsorption results in an optical signature that can be tracked with oneor more optical sensors placed along the conduit to determine the motionof the moving fluid in pixel/sec.

Accordingly, the system 100 may include a feature extraction module(e.g., within the sensor arrangement 110, the computing device 150, orany suitable combination thereof) that is or includes an algorithmicmodule configured to access the digital signal from an optical sensor oroptical measurement modality as the input and return a set of digitalsignals that represent one or more distinctive characteristics of thefluid for algorithmic analysis.

The feature extraction module extracts distinctive characteristics in amoving fluid. Extractable features include but are not limited to (i)moving fluid head, (ii) moving fluid tail, (iii) moving bubbles, (iv)other moving patterns or gradients, and (v) other spatial or temporalcues of movement (e.g. textures associated with movement, temporalvariation relative to spatial variation, blurriness, etc.).

In some variations, the feature extraction module extracts features intwo-dimensional (2D) space by computing the difference from twoconsecutive optical sensor data in chronological order. The extracted 2Ddifferential features will be further categorized into sub-categorieswith a computer vision algorithm or other machine learning algorithm(e.g., a deep learning algorithm).

In some variations, the feature extraction module extracts features inone-dimensional (1D) space by rearranging 2D digital data into 1Ddigital data before performing the differential feature extractions andcategorization.

In some variations, the feature extraction module extracts features inmulti-dimensional space by processing multiple digital data from morethan one source. Multi-dimensional features, such as three-dimensional(3D) features will be extracted and categorized by computer visionalgorithms or other variations of machine learning algorithms.

In some variations, the feature extraction module classifies andsegments the feature from optical sensor output using a deep learningmodel.

FIG. 14 is flowchart illustrating a method of performing logicaloperations in characterizing fluidic content flowing through a conduit,according to some example embodiments. Although this method isdescribed, by way of example, as being performed by the system 100, themethod may be performed by other hardware configurations. Accordingly,the system 100 may include a fluid displacement estimation module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to access the extracted feature and compute the displacementof fluid from one point in time to another point in time.

In some variations, the fluid displacement estimation module utilizesmathematical methods to compute the displacement of individual extractedfeatures from different timestamps. Cross-correlation or any othersimilar mathematical method is used to calculate the shift of the givenfeature in pixel/sec.

In some variations, the fluid displacement estimation module deploys adeep learning model to estimate fluid displacement from one or multipleoptical sensors.

The system 100 may also include a fluid motion model module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to estimate the flow of the fluidic content when there are nostrong features to track during a session of the fluid flow, such asduring laminar or continuous patches of fluid flow.

The fluid motion model module interpolates the motion of the fluidiccontent when the fluid displacement module did not output a confidentresult, such as in a laminar flow session, where there is little to nodistinct features for mathematical computation. The fluid motion modelmodule estimates displacement of the fluid from past fluid features,future fluid features (e.g., predicted fluid features), or both.

In some variations, the fluid motion model module is controlled byinternal parameters, including but not limited to cross-correlationstrength, feature size, and fluid occupancy. As shown in FIG. 14, thefluid motion model module runs through multiple logic blocks (e.g.,Block 1, Block 2, Block 3, Block 4, Block 5, and Block 6, or anysuitable subset thereof) to determine the confidence of the estimationfrom fluid displacement estimation module, as well as to re-estimate thefluid displacement with past and future (e.g., predicted) events.

In Block 1, according to some example embodiments, a decision 1410(e.g., a first decision) is made to determine whether a distinct fluidhead is present. If present, the start of a fluid chunk is indicated bythe fluid head. Otherwise, no fluid head is present, and the fluidmotion model proceeds to Block 2.

In Block 2, according to some example embodiments, a decision 1420(e.g., a second decision) is made to determine whether a distinct fluidtail is present. If present, the end of a fluid chunk is indicated bythe fluid tail. Otherwise, no fluid head or fluid tail is present, andthe fluid motion model proceeds to Block 3.

In Block 3, according to some example embodiments, a decision 1430(e.g., a third decision) is made to determine whether a distinctcorrelation result is present. If present, the fluid motion modelproceeds to Block 4. Otherwise, with no fluid head or fluid tailpresent, and with no distinct correlation result present, aclassification of fluid type is made to distinguish between laminar flowand no flow.

In Block 4, according to some example embodiments, a decision 1440(e.g., a fourth decision) is made to determine whether the distinctcorrelation result is accepted. If accepted, the fluid displacement isindicated by the correlation result. Otherwise, the fluid motion modelproceeds to Block 5.

In Block 5, according to some example embodiments, a decision 1450(e.g., a fifth decision) is made to determine whether a corner case(e.g., an exotic, erroneous, or otherwise specially excepted condition)is present. If present, the fluid displacement is indicated by anadjacent image (e.g., the next or previous video image in a sequence ofvideo images that depict the fluidic content in the conduit). Otherwise,the fluid motion model proceeds to Block 6.

In Block 6, according to some example embodiments, a decision 1460(e.g., a sixth decision) is made to determine whether extreme slowmotion of the fluidic content (e.g., with apparent displacement below athreshold of detection) is present. If extreme slow motion is present,the fluid displacement is treated as no flow (e.g., zero displacementfrom image to image or displacement below a threshold amount from imageto image) by the fluid motion model. Otherwise, the fluid displacementis indicated by an adjacent image (e.g., the next or previous videoimage in a sequence of video images that depict the fluidic content inthe conduit), which is likely to show more discernable apparentdisplacement than the current image.

FIG. 15 is a diagram illustrating optical mass estimation for fluidiccontent, according to some example embodiments.

The system 100 may include an optical mass estimation module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to analyze the measured substance in the fluidic content whenthe fluidic content is passing through a conduit (e.g., the conduit 120,250, 450, 550, 650, 750, 850, 1050, or 1150) paired with a sensorarrangement (e.g., the sensor arrangement 110, 210, or 310) with opticalsensors (e.g., CMOS or CCD) and LEDs. The measured substance can be afluid component of the fluidic content, and the fluid component may be ahomogeneous fluid or a heterogeneous fluid, or may have other fluidcharacteristics.

Assembly of the optical mass estimation module may generally involveplacing one or more LEDs or other light sources with differentintensities, wavelengths, orientations, or any suitable combinationthereof, at one end of a conduit segment (e.g., the conduit segment 350)and one or more photo-detectors or other optical sensors with the sameor different configurations at the other end of the conduit segment. Theoptical mass estimation module may first measure the absorbance ortransmittance and then utilize physical principles to analyze the targetsubstance (e.g., by applying Beer Lambert's Law to estimate theconcentration of the measured substance given its transmittance).

In some variations, multiple pairs of light sources and optical sensorsare utilized. For each optical sensor looking toward the conduit segment(e.g., the conduit segment 350), a light source is placed 180 degrees atthe opposite side of the conduit segment. The optical mass estimationmodule estimates the amount of light that is absorbed by the fluidiccontent that entered the region of interest of the conduit segment,which may be called the differential segment. The optical massestimation module uses the motion tracking algorithm to determine thelength of the differential segment, quantifies the average lightabsorbance in the differential segment, and applies a mathematicalderivation based on Beer-Lambert's Law.

A(δV)=a(λ)dc(δV)∝δm _(h)(t)  (7)

where A is absorbance we compute, δV=πd²δp is the differential volume,and δp is the differential segment length. The system 100 uses BeerLambert's law to express absorbance in terms of absorptivity a(λ) of themeasured substance, times the path length d times the concentration c ofmeasured substance in the fluid.

The optical mass estimation module then multiplies the computedabsorbance to the length (in pixels) of the differential segment toobtain an optical mass estimation per frame in arbitrary units (a.u.).The optical mass estimation module also integrates the optical massestimation per frame to obtain an optical mass estimation over time inarbitrary units (a.u.).

In some variations, the optical mass estimation module also estimatesthe optical mass of the measured substance with one or multiplecombinations of LEDs or other light sources, optical sensors, andconduit types.

FIG. 16 is a diagram illustrating estimation of fluid componentconcentration based on particle scattering, according to some exampleembodiments.

The system 100 may include a fluid scattering estimation module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to determine the presence of scattering particles, which maybe performed as part of estimating hemoglobin concentration in blood atdifferent hemolysis levels that cause variations in scatteringparameters.

The fluid scattering estimation module utilizes a special heterogeneousillumination assembly to disambiguate the scattering and absorptionparameters of the fluidic content.

In some variations, heterogeneous illumination is achieved by (i)placing LEDs or other light sources apart at some distance, (ii) usingstructured LEDs or other light sources, or (iii) creating acustom-structured light source by passing uniform light through astructured grating. Heterogeneous illumination may create brighterpatches and darker patches when viewed by optical sensors. The fluidscattering estimation module analyzes the signal difference between thebrighter patches and the darker patches to estimate the scatteringparameter of the fluidic content (e.g., in combination with a computervision algorithm or other machine learning algorithm, such as a deeplearning algorithm).

In some variations, conduits of different types are used to createstructural differences for the illumination.

The system 100 may include an optical mass calibration module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to translate the measurement from an optical sensor to theconcentration or mass of the measured substance.

The measurement from the optical sensor (e.g., a video sensor) is in anarbitrary unit, and the resulting information is translated to obtainthe distinct property of the fluidic content, such as the concentrationof a fluid component of the fluidic content or the mass of the fluidcomponent.

In some variations, the optical mass calibration module translatesoptical mass into the mass of the measured substance (e.g., the fluidcomponent of interest, such as blood) by a trained mathematical model. Alearning machine is trained with a mathematical model by calibrating thedifferential optical mass against any ground truth measurement (e.g.,using a weighing scale). For each measured substance, the learningmachine can be trained by one or multiple datasets with differentconcentration combinations.

In some variations, the optical mass calibration module translatesoptical mass into the concentration of the measured substance (e.g., thefluid component of interest) by a trained mathematical model. Themathematical model may be trained by machine learning (e.g., using adeep learning algorithm).

The system 100 may include a fluid type classification module (e.g.,within the sensor arrangement 110, the computing device 150, or anysuitable combination thereof) that is or includes an algorithmic moduleconfigured to classify the fluidic contents (e.g., determine a fluidtype of the fluidic contents) within a given time frame.

The fluid type classification module automatically categorizes differentfluidic content with different properties based on the output of thesensor arrangement or other measuring modality. The classification canbe done at any given time frame with supplemental algorithmic logic.

In some variations, the fluid type classification module classifieslaminar and non-laminar flow from an optical sensor.

FIG. 17 is a graph illustrating a mathematical merge of results fromvarious types of sensors, according to some example embodiments.

The system 100 may include a sensor merging module (e.g., within thesensor arrangement 110, the computing device 150, or any suitablecombination thereof) that is or includes an algorithmic moduleconfigured to combine measurement of measured substance betweendifferent sensors (e.g., with different measuring modalities).

To maximize the advantages of different sensors with differentproperties, a mathematical merge function is used to appoint a weight ofimportance on different types of sensors. The mathematical mergefunction is derived from the properties and the condition of the fluidiccontent.

In some variations, the sensor merging module combines measurementbetween an ultrasound sensor and an optical sensor using a mergefunction derived from the fluid type classification module and theweight of accuracy on different sensors at different fluid conditions.

Any one or more of the components (e.g., modules) described herein maybe implemented using hardware alone (e.g., one or more processors) or acombination of hardware and software. For example, any componentdescribed herein may physically include an arrangement of one or moreprocessors configured to perform the operations described herein forthat component. As another example, any component described herein mayinclude software, hardware, or both, that configure an arrangement ofone or more processors to perform the operations described herein forthat component. Accordingly, different components described herein mayinclude and configure different arrangements of processors at differentpoints in time or a single arrangement of such processors at differentpoints in time. Each component (e.g., module) described herein is anexample of a means for performing the operations described herein forthat component. Moreover, any two or more components described hereinmay be combined into a single component, and the functions describedherein for a single component may be subdivided among multiplecomponents. Furthermore, according to various example embodiments,components described herein as being implemented within a single systemor machine (e.g., a single device) may be distributed across multiplesystems or machines (e.g., multiple devices).

Any of the systems or machines (e.g., devices) discussed herein may be,include, or otherwise be implemented in a special-purpose (e.g.,specialized or otherwise non-conventional and non-generic) computer thathas been modified to perform one or more of the functions describedherein for that system or machine (e.g., configured or programmed byspecial-purpose software, such as one or more software modules of aspecial-purpose application, operating system, firmware, middleware, orother software program). For example, a special-purpose computer systemable to implement any one or more of the methodologies described hereinis discussed below with respect to FIG. 18, and such a special-purposecomputer may accordingly be a means for performing any one or more ofthe methodologies discussed herein. Within the technical field of suchspecial-purpose computers, a special-purpose computer that has beenspecially modified (e.g., configured by special-purpose software) by thestructures discussed herein to perform the functions discussed herein istechnically improved compared to other special-purpose computers thatlack the structures discussed herein or are otherwise unable to performthe functions discussed herein. Accordingly, a special-purpose machineconfigured according to the systems and methods discussed hereinprovides an improvement to the technology of similar special-purposemachines. Moreover, any two or more of the systems or machines discussedherein may be combined into a single system or machine, and thefunctions described herein for any single system or machine may besubdivided among multiple systems or machines.

FIG. 18 is a block diagram illustrating components of a machine 1800(e.g., the computing device 150), according to some example embodiments,able to read instructions 1824 from a machine-readable medium 1822(e.g., a non-transitory machine-readable medium, a machine-readablestorage medium, a computer-readable storage medium, or any suitablecombination thereof) and perform any one or more of the methodologiesdiscussed herein, in whole or in part. Specifically, FIG. 18 shows themachine 1800 in the example form of a computer system (e.g., a computer)within which the instructions 1824 (e.g., software, a program, anapplication, an applet, an app, or other executable code) for causingthe machine 1800 to perform any one or more of the methodologiesdiscussed herein may be executed, in whole or in part.

In alternative embodiments, the machine 1800 operates as a standalonedevice or may be communicatively coupled (e.g., networked) to othermachines. In a networked deployment, the machine 1800 may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a distributed (e.g.,peer-to-peer) network environment. The machine 1800 may be a servercomputer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a cellular telephone, a smartphone, a set-top box (STB), a personal digital assistant (PDA), a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1824, sequentially orotherwise, that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute the instructions 1824 to perform all or part of any oneor more of the methodologies discussed herein.

The machine 1800 includes a processor 1802 (e.g., one or more centralprocessing units (CPUs), one or more graphics processing units (GPUs),one or more digital signal processors (DSPs), one or more applicationspecific integrated circuits (ASICs), one or more radio-frequencyintegrated circuits (RFICs), or any suitable combination thereof), amain memory 1804, and a static memory 1806, which are configured tocommunicate with each other via a bus 1808. The processor 1802 containssolid-state digital microcircuits (e.g., electronic, optical, or both)that are configurable, temporarily or permanently, by some or all of theinstructions 1824 such that the processor 1802 is configurable toperform any one or more of the methodologies described herein, in wholeor in part. For example, a set of one or more microcircuits of theprocessor 1802 may be configurable to execute one or more modules (e.g.,software modules) described herein. In some example embodiments, theprocessor 1802 is a multicore CPU (e.g., a dual-core CPU, a quad-coreCPU, an 8-core CPU, or a 128-core CPU) within which each of multiplecores behaves as a separate processor that is able to perform any one ormore of the methodologies discussed herein, in whole or in part.Although the beneficial effects described herein may be provided by themachine 1800 with at least the processor 1802, these same beneficialeffects may be provided by a different kind of machine that contains noprocessors (e.g., a purely mechanical system, a purely hydraulic system,or a hybrid mechanical-hydraulic system), if such a processor-lessmachine is configured to perform one or more of the methodologiesdescribed herein.

The machine 1800 may further include a graphics display 1810 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1800 may also include an alphanumeric input device 1812 (e.g., akeyboard or keypad), a pointer input device 1814 (e.g., a mouse, atouchpad, a touchscreen, a trackball, a joystick, a stylus, a motionsensor, an eye tracking device, a data glove, or other pointinginstrument), a data storage 1316, an audio generation device 1318 (e.g.,a sound card, an amplifier, a speaker, a headphone jack, or any suitablecombination thereof), and a network interface device 1820.

The data storage 1316 (e.g., a data storage device) includes themachine-readable medium 1822 (e.g., a tangible and non-transitorymachine-readable storage medium) on which are stored the instructions1824 embodying any one or more of the methodologies or functionsdescribed herein. The instructions 1824 may also reside, completely orat least partially, within the main memory 1804, within the staticmemory 1806, within the processor 1802 (e.g., within the processor'scache memory), or any suitable combination thereof, before or duringexecution thereof by the machine 1800. Accordingly, the main memory1804, the static memory 1806, and the processor 1802 may be consideredmachine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1824 may be transmitted orreceived over a network 1890 via the network interface device 1820. Forexample, the network interface device 1820 may communicate theinstructions 1824 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1800 may be a portablecomputing device (e.g., a smart phone, a tablet computer, or a wearabledevice), and may have one or more additional input components 1830(e.g., sensors or gauges). Examples of such input components 1830include an image input component (e.g., one or more cameras), an audioinput component (e.g., one or more microphones), a direction inputcomponent (e.g., a compass), a location input component (e.g., a globalpositioning system (GPS) receiver), an orientation component (e.g., agyroscope), a motion detection component (e.g., one or moreaccelerometers), an altitude detection component (e.g., an altimeter), atemperature input component (e.g., a thermometer), and a gas detectioncomponent (e.g., a gas sensor). Input data gathered by any one or moreof these input components 1830 may be accessible and available for useby any of the modules described herein (e.g., with suitable privacynotifications and protections, such as opt-in consent or opt-outconsent, implemented in accordance with user preference, applicableregulations, or any suitable combination thereof).

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1822 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofcarrying (e.g., storing or communicating) the instructions 1824 forexecution by the machine 1800, such that the instructions 1824, whenexecuted by one or more processors of the machine 1800 (e.g., processor1802), cause the machine 1800 to perform any one or more of themethodologies described herein, in whole or in part. Accordingly, a“machine-readable medium” refers to a single storage apparatus ordevice, as well as cloud-based storage systems or storage networks thatinclude multiple storage apparatus or devices. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, one or more tangible and non-transitory data repositories(e.g., data volumes) in the example form of a solid-state memory chip,an optical disc, a magnetic disc, or any suitable combination thereof.

A “non-transitory” machine-readable medium, as used herein, specificallyexcludes propagating signals per se. According to various exampleembodiments, the instructions 1824 for execution by the machine 1800 canbe communicated via a carrier medium (e.g., a machine-readable carriermedium). Examples of such a carrier medium include a non-transientcarrier medium (e.g., a non-transitory machine-readable storage medium,such as a solid-state memory that is physically movable from one placeto another place) and a transient carrier medium (e.g., a carrier waveor other propagating signal that communicates the instructions 1824).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module in which the hardware includes one or more processors.Accordingly, the operations described herein may be at least partiallyprocessor-implemented, hardware-implemented, or both, since a processoris an example of hardware, and at least some operations within any oneor more of the methods discussed herein may be performed by one or moreprocessor-implemented modules, hardware-implemented modules, or anysuitable combination thereof.

Moreover, such one or more processors may perform operations in a “cloudcomputing” environment or as a service (e.g., within a “software as aservice” (SaaS) implementation). For example, at least some operationswithin any one or more of the methods discussed herein may be performedby a group of computers (e.g., as examples of machines that includeprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)). The performance of certainoperations may be distributed among the one or more processors, whetherresiding only within a single machine or deployed across a number ofmachines. In some example embodiments, the one or more processors orhardware modules (e.g., processor-implemented modules) may be located ina single geographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, the one ormore processors or hardware modules may be distributed across a numberof geographic locations.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures and theirfunctionality presented as separate components and functions in exampleconfigurations may be implemented as a combined structure or componentwith combined functions. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents and functions. These and other variations, modifications,additions, and improvements fall within the scope of the subject matterherein.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a memory (e.g., acomputer memory or other machine memory). Such algorithms or symbolicrepresentations are examples of techniques used by those of ordinaryskill in the data processing arts to convey the substance of their workto others skilled in the art. As used herein, an “algorithm” is aself-consistent sequence of operations or similar processing leading toa desired result. In this context, algorithms and operations involvephysical manipulation of physical quantities. Typically, but notnecessarily, such quantities may take the form of electrical, magnetic,or optical signals capable of being stored, accessed, transferred,combined, compared, or otherwise manipulated by a machine. It isconvenient at times, principally for reasons of common usage, to referto such signals using words such as “data,” “content,” “bits,” “values,”“elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” orthe like. These words, however, are merely convenient labels and are tobe associated with appropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “accessing,” “processing,” “detecting,” “computing,”“calculating,” “determining,” “generating,” “presenting,” “displaying,”or the like refer to actions or processes performable by a machine(e.g., a computer) that manipulates or transforms data represented asphysical (e.g., electronic, magnetic, or optical) quantities within oneor more memories (e.g., volatile memory, non-volatile memory, or anysuitable combination thereof), registers, or other machine componentsthat receive, store, transmit, or display information. Furthermore,unless specifically stated otherwise, the terms “a” or “an” are hereinused, as is common in patent documents, to include one or more than oneinstance. Finally, as used herein, the conjunction “or” refers to anon-exclusive “or,” unless specifically stated otherwise.

The following enumerated descriptions describe various examples ofmethods, machine-readable media, and systems (e.g., machines, devices,or other apparatus) discussed herein.

A first example provides a method for characterizing patient fluid lossby a patient, the method comprising:

quantifying flow of fluidic content through a conduit, the fluidiccontent comprising a patient fluid;estimating a concentration of a fluid component in the patient fluid;andcharacterizing patient fluid loss based at least in part on thequantified flow and the concentration of the fluid component,wherein at least one of the flow rate and the concentration of the fluidcomponent is based on sampling data from a sensor arrangement coupled tothe conduit.

A second example provides a method according to the first example,wherein the patient fluid is blood, and the fluid component ishemoglobin.

A third example provides a method according to the first example or thesecond example, wherein characterizing patient fluid loss comprisesquantifying volume of blood flowing through the conduit.

A fourth example provides a method according to any of the first throughthird examples, further comprising measuring composition of the fluidiccontent.

A fifth example provides a method according to the fourth example,wherein measuring composition of the fluidic content comprisesperforming spectroscopic analysis.

A sixth example provides a method according to any of the first throughfifth examples, wherein quantifying flow of fluidic content comprisesestimating a flow rate of the fluidic content.

A seventh example provides a method according to the sixth example,wherein estimating the flow rate comprises emitting ultrasonic wavesinto the conduit and analyzing a frequency shift of ultrasonic wavesreflected from the fluidic content.

An eighth example provides a method according to the sixth example,wherein estimating the flow rate comprises emitting ultrasonic wavesinto the conduit and analyzing time of flight of ultrasonic wavestransmitted through the fluidic content.

A ninth example provides a method according to the sixth example,wherein estimating the flow rate comprises comparing a first opticalsignal and a second optical signal, the first optical signalcorresponding to light detected at a first location along the conduitand the second optical signal corresponding to light detected at asecond location along the conduit.

A tenth example provides a method according to any of the first throughninth examples, wherein quantifying flow of fluidic content comprisesestimating a thermal mass flow of the fluidic content.

An eleventh example provides a method according to any of the firstthrough tenth examples, wherein estimating the concentration of a fluidcomponent comprises analyzing a multispectral image of the conduit.

A twelfth example provides a method according to any of the firstthrough eleventh examples, further comprising reducing the flow of thefluidic content through the conduit while quantifying the flow of thefluidic content.

A thirteenth example provides a system for characterizing patient fluidloss by a patient, the system comprising:

a sensor arrangement couplable to a conduit, the sensor arrangementcomprising:at least one sensor configured to quantify flow of fluidic contentthrough the conduit, the fluidic content comprising a patient fluid, andto estimate a concentration of a fluid component in the patient fluid.

A fourteenth example provides a system according to the thirteenthexample, wherein the sensor arrangement comprises a housing configuredto cover at least a portion of the conduit.

A fifteenth example provides a system according to the thirteenthexample or the fourteenth example, wherein the housing comprises jawsconfigured to clamp onto the conduit.

A sixteenth example provides a system according to any of the thirteenththrough fifteenth examples, further comprising a conduit insertcouplable inline with the conduit.

A seventeenth example provides a system according to any of thethirteenth through sixteenth examples, wherein the at least one sensorcomprises an ultrasound flow rate sensor.

An eighteenth example provides a system according to any of thethirteenth through seventeenth examples, wherein the at least one sensorcomprises an optical sensor configured to detect light transmittedthrough the fluidic content.

A nineteenth example provides a system according to any of thethirteenth through eighteenth examples, wherein the sensor arrangementcomprises a plurality of optical sensors arranged at a plurality ofaxial locations along the conduit.

A twentieth example provides a system according to any of the thirteenththrough nineteenth examples, wherein the sensor arrangement comprises aplurality of optical sensors arranged at a plurality of circumferentiallocations around the conduit.

A twenty-first example provides a system according to any of thethirteenth through twentieth examples, wherein the sensor arrangementcomprises a plurality of optical sensors arranged helically around theconduit.

A twenty-second example provides a system according to any of thethirteenth through twenty-first examples, wherein the at least onesensor comprises a thermal mass flow sensor.

A twenty-third example provides a system according to any of thethirteenth through twenty-second examples, wherein the at least onesensor comprises an optical sensor array configured to performmultispectral imaging.

A twenty-fourth example provides a system according to any of thethirteenth through twenty-third examples, wherein the at least onesensor comprises an optical sensor array configured to perform colorimaging.

A twenty-fifth example provides a system according to any of thethirteenth through twenty-fourth examples, further comprising aprocessor configured to characterize patient fluid loss based at leastin part on the quantified flow and the concentration of the fluidcomponent.

A twenty-sixth example provides a carrier medium carryingmachine-readable instructions for controlling a machine to carry out theoperations (e.g., method operations) performed in any one of thepreviously described examples.

What is claimed is:
 1. A method comprising: accessing sensor data from asensor arrangement included in a housing configured to cover at least aportion of a conduit through which fluidic content is flowing, thefluidic content including a patient fluid, the housing positioning thesensor arrangement proximate to the conduit; quantifying flow of thefluidic content flowing through the conduit; estimating a concentrationof a fluid component of the patient fluid in the fluidic content flowingthrough the conduit; and by one or more processors, characterizingpassage of the patient fluid through the conduit based on the quantifiedflow of the fluidic content and on the estimated concentration of thefluid component in the fluidic content, at least one of the quantifyingof the flow or the estimating of the concentration being based on thesensor data from the sensor arrangement positioned proximate to theconduit by the housing.
 2. The method of claim 1, wherein the patientfluid is blood, and the fluid component is hemoglobin.
 3. The method ofclaim 1 or claim 2, wherein the characterizing of the passage of thepatient fluid includes quantifying a volume of blood flowing through theconduit.
 4. The method of claim 1, wherein the estimating of theconcentration of the fluid component in the fluidic content flowingthrough the conduit includes measuring a composition of the fluidiccontent.
 5. The method of claim 4, wherein the measuring of thecomposition of the fluidic content includes performing a spectroscopicanalysis of the fluidic content flowing through the conduit.
 6. Themethod of claim 1, wherein the quantifying of the flow of the fluidiccontent includes estimating a volumetric flow rate of the fluidiccontent.
 7. The method of claim 6, wherein the estimating of thevolumetric flow rate of the fluidic content includes emitting outgoingultrasonic waves into the conduit and determining a frequency shift ofincoming ultrasonic waves reflected from the fluidic content.
 8. Themethod of claim 6, wherein the estimating of the volumetric flow rate ofthe fluidic content includes emitting ultrasonic waves into the conduitand determining a time of flight of ultrasonic waves transmitted throughthe fluidic content.
 9. The method of claim 6, wherein the estimating ofthe volumetric flow rate of the fluidic content includes comparing afirst optical signal to a second optical signal, the first opticalsignal indicating light detected at a first location along the conduit,the second optical signal indicating light detected at a second locationalong the conduit.
 10. The method of claim 1, wherein the quantifying ofthe flow of the fluidic content includes estimating a mass flow rate ofthe fluidic content.
 11. The method of claim 1, wherein the estimatingof the concentration of the fluid component in the fluidic content isbased on a multispectral image of the conduit in which the fluidiccontent is flowing.
 12. The method of claim 1, further comprisingreducing the flow of the fluidic content through the conduit; andwherein the quantifying of the flow of the fluidic content quantifiesthe reduced flow of the fluidic content.
 13. The method of claim 1,wherein: the flow of the fluidic content through the conduit includes afirst region of continuous flow and a second region of non-continuousflow; the sensor data includes a first output from an ultrasound sensorbased on the first region of continuous flow and a second output from anoptical sensor based on the second region of non-continuous flow; andthe quantifying of the flow of the fluidic content includes estimating aflow rate based on the first output from the ultrasound sensor and basedon the second output from the optical sensor.
 14. The method of claim 1,wherein: the quantifying of the flow of the fluidic content includesinputting the sensor data into a learning machine trained to quantifycandidate flows based on a training set of reference flows representedby reference sensor data, the trained learning machine outputting thequantified flow of the fluidic content based on the inputted sensordata.
 15. A system comprising: a sensor arrangement included in ahousing configured to cover at least a portion of a conduit and positionthe sensor arrangement proximate to the conduit, the conduit beingconfigured to convey fluidic content that includes a patient fluid, thesensor arrangement including at least one sensor configured to generatesensor data based on the fluidic content; and one or more processorsconfigured to perform operations comprising: accessing the sensor datafrom the sensor arrangement positioned proximate to the conduit throughwhich the fluidic content that includes the patient fluid is flowing;quantifying flow of the fluidic content flowing through the conduit;estimating a concentration of a fluid component of the patient fluid inthe fluidic content flowing through the conduit; and characterizingpassage of the patient fluid through the conduit based on the quantifiedflow of the fluidic content and on the estimated concentration of thefluid component in the fluidic content, at least one of the quantifyingof the flow or the estimating of the concentration being based on thesensor data from the sensor arrangement positioned proximate to theconduit by the housing.
 16. The system of claim 15, the housing includesa conduit seat configured to receive at least a portion of the conduit.17. The system of claim 15, wherein the housing includes a clampconfigured to clamp onto the conduit.
 18. The system of claim 15,wherein the conduit configured to convey the fluidic content includes aconduit insert that is couplable inline with at least one other conduitsegment configured to convey the fluidic content.
 19. The system ofclaim 15, wherein the at least one sensor in the sensor arrangementpositioned by the housing proximate to the conduit includes anultrasound sensor configured to output a volumetric flow rate of thefluidic content.
 20. The system of claim 15, wherein the at least onesensor in the sensor arrangement positioned by housing proximate to theconduit includes an optical sensor configured to detect lighttransmitted through the fluidic content.
 21. The system of claim 15,wherein the at least one sensor in the sensor arrangement positioned bythe housing proximate to the conduit includes a plurality of opticalsensors arranged at a plurality of axial locations along the conduit.22. The system of claim 15, wherein the at least one sensor in thesensor arrangement positioned by the housing proximate to the conduitincludes a plurality of optical sensors arranged at a plurality ofcircumferential locations around the conduit.
 23. The system of claim15, wherein the at least one sensor in the sensor arrangement positionedby the housing proximate to the conduit includes a plurality of opticalsensors arranged helically around the conduit.
 24. The system of claim15, wherein the at least one sensor in the sensor arrangement positionedby the housing proximate to the conduit includes a thermal mass flowsensor configured to output a mass flow rate of the fluidic content. 25.The system of claim 15, wherein the at least one sensor in the sensorarrangement positioned by the housing proximate to the conduit includesan array of optical sensors configured to perform multispectral imagingof the fluidic content.
 26. The system of claim 15, wherein the at leastone sensor in the sensor arrangement positioned by the housing proximateto the conduit includes an array of optical sensors configured toperform color imaging of the fluidic content.
 27. The system of claim15, wherein the operations performed by the one or more processorsfurther comprise: causing a display to present an alert based on thecharacterized passage of the patient fluid through the conduit.
 28. Thesystem of claim 15, wherein: the flow of the fluidic content through theconduit includes a first region of continuous flow and a second regionof non-continuous flow; the sensor data includes a first output from anultrasound sensor based on the first region of continuous flow and asecond output from an optical sensor based on the second region ofnon-continuous flow; and the quantifying of the flow of the fluidiccontent includes estimating a flow rate based on the first output fromthe ultrasound sensor and based on the second output from the opticalsensor.
 29. The system of claim 15, wherein the operations performed bythe one or more processors further comprise: the quantifying of the flowof the fluidic content includes inputting the sensor data into alearning machine trained to quantify candidate flows based on a trainingset of reference flows represented by reference sensor data, the trainedlearning machine outputting the quantified flow of the fluidic contentbased on the inputted sensor data.
 30. A machine-readable storage mediumcomprising instructions that, when executed by one or more processors ofa machine, cause the machine to perform operations comprising: accessingsensor data from a sensor arrangement included in a housing configuredto cover at least a portion of a conduit through which fluidic contentis flowing, the fluidic content including a patient fluid, the housingpositioning the sensor arrangement proximate to the conduit; quantifyingflow of the fluidic content flowing through the conduit; estimating aconcentration of a fluid component in the fluidic content flowingthrough the conduit; and characterizing passage of the patient fluidthrough the conduit based on the quantified flow of the fluidic contentand on the estimated concentration of the fluid component in the fluidiccontent, at least one of the quantifying of the flow or the estimatingof the concentration being based on the sensor data from the sensorarrangement positioned proximate to the conduit by the housing.